TY - JOUR
T1 - Learning the exception to the rule
T2 - Model-based fMRI reveals specialized representations for surprising category members
AU - Davis, Tyler
AU - Love, Bradley C.
AU - Preston, Alison R.
PY - 2012/2
Y1 - 2012/2
N2 - Category knowledge can be explicit, yet not conform to a perfect rule. For example, a child may acquire the rule ''If it has wings, then it is a bird,'' but then must account for exceptions to this rule, such as bats. The current study explored the neurobiological basis of rule-plus-exception learning by using quantitative predictions from a category learning model, SUSTAIN, to analyze behavioral and functional magnetic resonance imaging (fMRI) data. SUSTAIN predicts that exceptions require formation of specialized representations to distinguish exceptions from rule-following items in memory. By incorporating quantitative trial-by-trial predictions from SUSTAIN directly into fMRI analyses, we observed medial temporal lobe (MTL) activation consistent with 2 predicted psychological processes that enable exception learning: item recognition and error correction. SUSTAIN explains how these processes vary in the MTL across learning trials as category knowledge is acquired. Importantly, MTL engagement during exception learning was not captured by an alternate exemplar-based model of category learning or by standard contrasts comparing exception and rulefollowing items. The current findings thus provide a well-specified theory for the role of the MTL in category learning, where the MTL plays an important role in forming specialized category representations appropriate for the learning context.
AB - Category knowledge can be explicit, yet not conform to a perfect rule. For example, a child may acquire the rule ''If it has wings, then it is a bird,'' but then must account for exceptions to this rule, such as bats. The current study explored the neurobiological basis of rule-plus-exception learning by using quantitative predictions from a category learning model, SUSTAIN, to analyze behavioral and functional magnetic resonance imaging (fMRI) data. SUSTAIN predicts that exceptions require formation of specialized representations to distinguish exceptions from rule-following items in memory. By incorporating quantitative trial-by-trial predictions from SUSTAIN directly into fMRI analyses, we observed medial temporal lobe (MTL) activation consistent with 2 predicted psychological processes that enable exception learning: item recognition and error correction. SUSTAIN explains how these processes vary in the MTL across learning trials as category knowledge is acquired. Importantly, MTL engagement during exception learning was not captured by an alternate exemplar-based model of category learning or by standard contrasts comparing exception and rulefollowing items. The current findings thus provide a well-specified theory for the role of the MTL in category learning, where the MTL plays an important role in forming specialized category representations appropriate for the learning context.
KW - Category learning
KW - Category representation
KW - Exception learning
KW - Hippocampus
KW - Medial temporal lobe
KW - SUSTAIN
UR - http://www.scopus.com/inward/record.url?scp=84863484247&partnerID=8YFLogxK
U2 - 10.1093/cercor/bhr036
DO - 10.1093/cercor/bhr036
M3 - Article
C2 - 21666132
AN - SCOPUS:84863484247
SN - 1047-3211
VL - 22
SP - 260
EP - 273
JO - Cerebral Cortex
JF - Cerebral Cortex
IS - 2
ER -